The wide spread use of wireless devices in a network environment has increased the demand for wireless local area networks (“WLANs”) to provide high data rates at low cost. Complementary Code Keying (CCK) is one communication technique that can be used to meet this demand. CCK uses complementary polyphase codes for modulating digital information, which has been adopted by IEEE 802.11b as the modulation scheme for WLANs operating at 5.5 Mbit/s and 11 Mbit/s data rates in a 2.4 GHz band. These types of codes provide complementary sequences (“symbols”) having phase parameters, and possess symmetry ideal for transmitting digital information. Typically, at a high data rate of 11 Mbit/s, the codes are grouped as “codewords” having 8-chips or a code length of 8. These codewords are a type of block code (“block codewords”). In this case, 256 possible combinations of codewords may be used. Communication systems can thus extract digital information from a received signal modulated with CCK codewords by decoding the CCK codewords.
One prior complementary code decoder is described in U.S. Pat. Nos. 5,841,813 to van Nee and 5,862,182 to Awater et al., which extracts information data of a CCK codeword by correlating different samples of the received signal. For such a decoder, the signal-to-noise ratios degrade significantly after the differential correlators. Additionally, the decoder is not applicable for transmission of CCK codes in multipath environments.
One possible decoding scheme for block codes is to match the received signal with all possible code patterns by correlators. A disadvantage of such a decoding scheme is that its complexity increases if the size of the block code is too large. Furthermore, this is an inefficient manner of decoding block codes such as CCK code. A low-complexity decoder for CCK was introduced by M. Webster and C. Andren, Harris/Lucent CCK description: additional cover code and fast transform detail, IEEE 802.11-98/331, September 1998, in which only a subset of CCK codewords are required to be correlated with the received signal by using a fast Walsh transform. However, this type of low-complexity decoder does not adequately address the problems caused by interference in multipath environments when decoding CCK codes.
For instance, in multipath environments such as inside an office building, the delay spread of a received signal can cause interference during decoding of CCK codes and symbols within each CCK codeword contained in the received signal. In particular, multipath distortion caused by signals being reflected off of walls within the building can result in propagation delay of the received signal. This type of distortion or interference regarding CCK codewords is referred to as inter-symbol interference (ISI). Two types of ISI can occur: inter-codeword interference and intra-codeword interference. Inter-codeword interference is signal interference between codewords. Intra-codeword interference is signal interference between symbols within a codeword.
One prior receiver is described in U.S. Pat. No. 6,233,273 to Webster et al. that deals with inter-codeword and intra-codeword interference. This receiver is a channel-matched correlation receiver (“RAKE”) that uses a decision feedback equalizer to mitigate the effects of multipath distortion. A disadvantage of this receiver is that it requires high signal-to-noise ratios, but a low-signal-to-noise ratios error propagation in the decision feedback equalizer causes chip errors to occur in bursts. This degrades the reliability of decoding CCK codewords. Thus, to handle low signal-to-noise ratios, the RAKE receiver is required to examine all received codeword chips prior to making a codeword decision, which is an inefficient manner of decoding CCK codewords.
One prior decoding technique has been introduced to decode and correct errors found in a signal encoded by a convolutional code. Convolutional code, unlike CCK codewords, is a continuous stream of data such as satellite data. This technique is commonly referred to as “Viterbi Decoding” that uses a trellis diagram to find a maximum-likelihood path recursively over the trellis diagram to decode convolutional data, as described in A. J. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, vol. IT-13, pp. 260-269, April 1967.
Thus, what is needed is a block code decoder that can use Viterbi decoding techniques in order to reduce the computational complexity for decoding block codes and to handle multipath distortion in multipath environments or on multiple types of channels.